Selecting critical features for data classification based on machine learning methods
Feature selection becomes prominent, especially in the data sets with many variables and
features. It will eliminate unimportant variables and improve the accuracy as well as the …
features. It will eliminate unimportant variables and improve the accuracy as well as the …
Review of preprocessing methods for univariate volatile time-series in power system applications
Outlier detection and correction of time-series referred to as preprocessing, play a vital role
in forecasting in power systems. Rigorous research on this topic has been made in the past …
in forecasting in power systems. Rigorous research on this topic has been made in the past …
Prediction of status particulate matter 2.5 using state Markov chain stochastic process and HYBRID VAR-NN-PSO
Air pollution is the entry or inclusion of living things, energy substances, and other
components into the air. Moreover, Air pollution is the presence of one or several …
components into the air. Moreover, Air pollution is the presence of one or several …
[PDF][PDF] Employing best input SVR robust lost function with nature-inspired metaheuristics in wind speed energy forecasting
Wind power has been experiencing a quick improvement. Without a doubt, wind is a
variable asset that is hard to forecast. For instance, traditionally time series, extra holds are …
variable asset that is hard to forecast. For instance, traditionally time series, extra holds are …
[PDF][PDF] An end to end of scalable tree boosting system
Feature selection in the health sector is essential to do. Moreover, an analysis of which
variables are indeed important that affect specific diseases. In the 20th century, many …
variables are indeed important that affect specific diseases. In the 20th century, many …
[PDF][PDF] Evaluation performance of SVR genetic algorithm and hybrid PSO in rainfall forecasting
Climate is an essential natural factor which is dynamic and challenging to predict. The
accurate climate prediction is needed. In this paper, we use support vector regression (SVR) …
accurate climate prediction is needed. In this paper, we use support vector regression (SVR) …
[HTML][HTML] DualLSTM: A novel key-quality prediction for a hierarchical cone thickener
Due to the inaccuracy and significant disturbance of the complex and harsh environment in
real industrial processes, the traditional sensor devices cannot meet the high-performance …
real industrial processes, the traditional sensor devices cannot meet the high-performance …
Evolving Hybrid Cascade Neural Network Genetic Algorithm Space–Time Forecasting
Design: At the heart of time series forecasting, if nonlinear and nonstationary data are
analyzed using traditional time series, the results will be biased. At the same time, if just …
analyzed using traditional time series, the results will be biased. At the same time, if just …
Hybrid time series and artificial neural network models for forecasting of the banking stock prices during Covid-19 pandemic
The stocks are one of a variety of securities that are traded in general through the stock
exchange. One sector that is quite large in the Indonesian capital market is banking stocks …
exchange. One sector that is quite large in the Indonesian capital market is banking stocks …
Evolving Hybrid Generalized Space-Time Autoregressive Forecasting with Cascade Neural Network Particle Swarm Optimization
Background: The generalized space-time autoregressive (GSTAR) model is one of the most
widely used models for modeling and forecasting time series and location data. Methods: In …
widely used models for modeling and forecasting time series and location data. Methods: In …